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Abstract #1560

Patch-based dictionaries for parallel MRI reconstruction

Jose Caballero 1 , Anthony N. Price 2,3 , Daniel Rueckert 1 , and Joseph V. Hajnal 2,3

1 Department of Computing, Imperial College London, London, United Kingdom, 2 Division of Imaging Sciences and Biomedical Engineering Department, King's College London, London, United Kingdom, 3 Centre for the Developing Brain, King's College London, London, United Kingdom

Acceleration of Magnetic Resonance (MR) acquisitions through partially parallel imaging using array coils is limited by noise amplification. Compressed sensing regularization has de-noising properties that can mitigate this effect. Recent results on dictionary learning have shown that using overcomplete patch-based frames and adapting them to the object can have a notable impact on reconstruction by finding sparser representations and adjusting to the natural features of the object. However, these results have not yet been tested for parallel MR. Here we propose an algorithm to exploit overcomplete and adaptive frames for SPIRiT reconstruction and demonstrate its superiority to traditional wavelet regularization.

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